Jev AI: Build Reliable AI Decision Systems with Jev AI

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Jev AI: Build Reliable AI Decision Systems with Jev AI
Last updated 9/2026
Created by Tom Phillips, WebDevEducation (Tom Phillips)
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 13 Lectures ( 55m ) | Size: 1.2 GB

Use Jev AI to build real AI decision systems, with TypeSafe Jev AI, decision logic, validation + real-world use cases

What you'll learn
⚡ Connect Jev to a modern Next.js and TypeScript application
⚡ Master Jev primitives including Noul, Choice, and Score
⚡ Build AI-powered decision logic using TypeSafe's Jev AI
⚡ Create structured decisions using a real-world project example
⚡ Build a supplement claims analyzer from scratch
⚡ Handle probabilities and confidence when making AI decisions

Requirements
❗ No previous experience with Jev or TypeSafe AI required
❗ A TypeSafe AI account and API key will be required during the course

Description
Learn how to useJev to build reliable, structured AI decision systems with TypeScript and Next.js.

In this course, you'll learnJev from the ground up and see how it can be used to make AI outputs more controlled, predictable, and useful inside real applications.

Instead of asking an LLM to return arbitrary free-form text,Jev lets you structure AI decisions around predefined questions, choices, scores, and application state. This makes Jev a strong fit for systems where you need AI to make consistent judgments that can be safely used by your application logic.

Throughout the course, you'll learn how to work with the coreJev primitives, structure questions effectively, pass relevant context into Jev, manage state, process multiple decisions, and combine AI judgments with deterministic TypeScript logic.

You'll build everything inside a modernNext.js application using TypeScript, @typesafe-ai/sdk, and shadcn/ui, so you'll see how Jev fits into a real full-stack development workflow rather than just isolated examples.

We'll also explore how to design more advancedJev workflows, including structured criteria, dynamic questions, scoring, validation, decision pipelines, and working with external domain knowledge.

A practical project is used throughout the course to demonstrate these concepts in a realistic application, taking you from your first Jev request through to a complete AI-powered decision workflow.

By the end of the course, you'll understand howJev works, how to integrate Jev into a Next.js application, and how to design structured AI decision systems for your own projects.

Whether you're building validation tools, compliance systems, classification workflows, risk assessments, content analysis, internal tools, or other AI-powered applications, the techniques in this course will give you a practical foundation for working withJev in real-world software.

Who this course is for
⭐ Anyone who want to learn Jev by building a practical, real-world project

Homepage

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